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A multivariate grain size and orientation distribution function: derivation from electron backscatter diffraction data and applications
Author(s) -
Galán López Jesús,
Kestens Leo A. I.
Publication year - 2021
Publication title -
journal of applied crystallography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.429
H-Index - 162
ISSN - 1600-5767
DOI - 10.1107/s1600576720014909
Subject(s) - electron backscatter diffraction , crystallite , materials science , grain size , orientation (vector space) , distribution function , diffraction , texture (cosmology) , range (aeronautics) , particle size distribution , multivariate statistics , field (mathematics) , statistical physics , microstructure , crystallography , computational physics , mathematics , optics , geometry , physics , statistics , thermodynamics , composite material , chemistry , metallurgy , computer science , particle size , image (mathematics) , artificial intelligence , pure mathematics
Two of the microstructural parameters most influential in the properties of polycrystalline materials are grain size and crystallographic texture. Although both properties have been extensively studied and there are a wide range of analysis tools available, they are generally considered independently, without taking into account the possible correlations between them. However, there are reasons to assume that grain size and orientation are correlated microstructural state variables, as they are the result of single microstructural formation mechanisms occurring during material processing. In this work, the grain size distribution and orientation distribution functions are combined in a single multivariate grain size orientation distribution function (GSODF). In addition to the derivation of the function, several examples of practical applications to low carbon steels are presented, in which it is shown how the GSODF can be used in the analysis of 2D and 3D electron backscatter diffraction data, as well as in the generation of representative volume elements for full‐field models and as input in simulations using mean‐field methods.